Abstract
Accurate spine segmentation is crucial for diagnosing and treating various spine diseases. Recently, Mamba-based methods have been widely applied in medical image segmentation. However, the spatial domain scanning strategy of Mamba fails to fully capture the fine anatomical structures and global dependencies of the spine. Moreover, existing frequency-enhanced methods often suffer from the loss of spatial localization. To address these challenges, we propose the Wavelet-Driven Spatial Frequency Mamba Network (WDSFM-Net). Specifically, we integrate the Discrete Wavelet Transform (DWT) into Mamba to construct the Spatial-Frequency Mamba Block (SFMB). By decomposing features into distinct frequency subbands, SFMB explicitly captures global structural context from low-frequency components while enhancing local anatomical details through high-frequency components. To accommodate specific spinal morphology, the Global Strip Pooling Attention (GSPA) module aggregates directional contexts to model the elongated and anisotropic spinal anatomy, while the Multi-Scale Attention Enhancement (MSAE) module employs multi-scale convolutions to adapt to significant vertebral scale variations. Additionally, we introduce a Dual-Domain Loss (DDL) function, which optimizes both spatial and frequency domain representations for robust training. We evaluated our WDSFM-Net on two public spine MRI datasets. The results show that the WDSFM-Net outperforms other state-of-the-art methods, achieving average Dice similarity coefficients of 0.8885 and 0.8669 in the Spider and MRSpine datasets, respectively.